Adaptive modeling of changed states in predictive condition monitoring

ABSTRACT

An improved empirical model-based surveillance or control system for monitoring or controlling a process or machine provides adaptation of the empirical model in response to new operational states that are deemed normal or non-exceptional for the process or machine. An adaptation decision module differentiates process or sensor upset requiring alerts from new operational states not yet modeled. A retraining module updates the empirical model to incorporate the new states, and a pruning technique optionally maintains the empirical model by removing older states in favor of the added new states recognized by the model.

CROSS REFERENCE TO RELATED APPLICATION

This application claims benefit of U.S. Provisional Application No.60/262,747, filed Jan. 19, 2001.

FIELD OF THE INVENTION

The present invention relates to monitoring machines and physicalprocesses for early detection of impending equipment failure or processdisturbance and on-line, continuous validation of sensor operation. Moreparticularly, the invention relates to systems and methods governingadaptation of empirical models to changed conditions in such monitoringsystems, and resolution of process change from sensor change.

BACKGROUND OF THE INVENTION

A variety of new and advanced techniques have emerged in industrialprocess control, machine control, system surveillance, and conditionbased monitoring to address drawbacks of traditionalsensor-threshold-based control and alarms. The traditional techniquesdid little more than provide responses to gross changes in individualmetrics of a process or machine, often failing to provide adequatewarning to prevent unexpected shutdowns, equipment damage, loss ofproduct quality or catastrophic safety hazards.

According to one branch of the new techniques, empirical models of themonitored process or machine are used in failure detection and control.Such models effectively leverage an aggregate view of surveillancesensor data to achieve much earlier incipient failure detection andfiner process control. By modeling the many sensors on a process ormachine simultaneously and in view of one another, the surveillancesystem can provide more information about how each sensor (and itsmeasured parameter) ought to be behaving. An example of such anempirical surveillance system is described in U.S. Pat. No. 5,764,509 toGross et al., the teachings of which are incorporated herein byreference. Therein is described an empirical model using a similarityoperator against a reference library of known states of the monitoredprocess, and an estimation engine for generating estimates of currentprocess states based on the similarity operation, coupled with asensitive statistical hypothesis test to determine if the currentprocess state is a normal or abnormal state. Other empirical model-basedmonitoring systems known in the art employ neural networks to model theprocess or machine being monitored.

Such empirical model-based monitoring systems require as part ofinstallation and implementation some baseline data characterizing thenormal operation of the process or machine under surveillance. Theempirical model embodies this baseline normal operational data, and isonly as good as the data represents normal operation. A big challenge tothe success of the empirical model in the monitoring system, therefore,is to provide sufficiently representative data when building theempirical model. In practice, this is possibly the greatest hurdle forsuccessful implementation of empirical model-based surveillance systems.

A first problem is whether to use data from merely a like process or theidentical process with the one being monitored. This is especiallysignificant when monitoring a commodity machine, that is, a machine thatwill be mass-produced with on-board condition monitoring. Under suchcircumstances, it may not be possible or practical to gather normaloperational data from each machine to build unique empirical modelsbeforehand. What is needed is a way of building a general model into thenewly minted machines, and allowing the model to adapt to the uniquetolerances and behavior of each particular machine in the field.

A second problem presents itself as the monitored process or machinesettles with age, drifting from the original normal baseline, but stillbeing in good operational condition. It is extremely difficult tocapture such eventually normal operational data from a process ormachine for which that would currently not constitute normal operation.What is then needed is a way for the empirical model to adapt toacceptable changes in the normal operation of the process or machinewith age, without sacrificing the monitoring sensitivity thatnecessitated the empirical model approach in the first place.

A third problem exists where it is not possible to capture the fullnormal operational range of sensor data from the process due to thefinancial or productive value of not disrupting the process. Forexample, in retrofitting an existing industrial process with empiricalmodel-based monitoring, it may not be economically feasible toeffectively take the process off-line and run it through its manyoperational modes. And it may be months or years before all theoperational modes are employed. Therefore, what is needed is a way toadapt the empirical model as the operational modes of the process ormachine are encountered for the first time.

In summary, in order for an empirical model based process surveillancesystem to function reliably, the data used to generate the model shouldspan the full process operating range. In many cases that data are notavailable initially. Therefore, model adaptation is needed to keep themodel up-to-date and valid. But adaptation imposes significant hurdlesof its own. One such hurdle is determining exactly when to startadapting the model, especially for dynamic non-linear processes. Whilein some cases human intervention can be relied upon to manually indicatewhen to adapt, in the vast majority of circumstances it is desirable toautomate this determination. Another such hurdle is determining when tostop adapting the model and reinitiate process or machine surveillance.Yet another problem is to distinguish the need for adaptation from aprocess upset or a sensor failure that should be properly alarmed on. Itis highly desirable to avoid “bootstrapping” on a slow drift fault inthe process, for example. Yet another problem is to avoid adaptingduring a period of transition between one stable state and another,during which sensor data may not be typically representative of eitherany old state or a new state of normal operation of the process ormachine. Yet another problem in adapting the empirical model is that themodel may grow and become less accurate or less specific due to theaddition of new states. Therefore, it would be beneficial to have a wayof removing least commonly encountered states from the model whileadding the newly adapted states.

SUMMARY OF THE INVENTION

The present invention provides an improved empirical model-based systemfor process or machine control and condition-based monitoring.

This invention is a method and apparatus for deciding when an empiricalmodel of a process or machine should be adapted to encompass changingstates in that process or machine, as measured by sensors, derivedvariables, statistical measures or the like. The technique is based onthe information provided by a similarity measurement technology andstatistical decisioning tools. This system has a second characteristicin that it determines when to stop the model adaptation process. Thissystem has a third capability of distinguishing between process changeand instrument change cases.

In a process or machine that is fully instrumented with sensors for allparameters of interest, sensor data is collected for all regimespossible of expected later operation of the same or similar processes ormachines. This collected data forms a history from which the inventivesystem can “learn” the desired or normal operation of the process ormachine, using training routines that distill it to a representative setof sensor data. Using this representative training set of sensor data,the present invention is able to monitor the process or machine inreal-time operation (or in batch mode, if preferred), and generateestimates for all the sensors, including certain of the sensors forwhich historic data was collected, but which have failed or which wereremoved from the process or machine. The present invention can beemployed as a means of adapting the representative set of sensor data toaccommodate changes to the monitored system that are considered withinthe acceptable or normal range of operation.

The apparatus of the present invention can be deployed as anelectrically powered device with memory and a processor, physicallylocated on or near the monitored process or machine. Alternatively, itcan be located remotely from the process or machine, as a module in acomputer receiving sensor data from live sensors on the process ormachine via a network or wireless transmission facility.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, however, as well asthe preferred mode of use, further objectives and advantages thereof, isbest understood by reference to the following detailed description ofthe embodiments in conjunction with the accompanying drawings, wherein:

FIG. 1 illustrates a block diagram of the invention for adapting anempirical model-based monitoring system for an instrumented process ormachine;

FIG. 2 illustrates a method for creating a representative “training”data set from collected sensor data for use in the invention;

FIG. 3 illustrates a flowchart for creating a representative “training”data set from collected sensor data for use in the invention;

FIG. 4 illustrates the computation of one of the similarity operatorsemployed in the present invention;

FIG. 5 illustrates a chart of global similarity values showing a moveinto transition out of one state of operation by the monitored processor machine;

FIG. 6 illustrates a chart of global similarity values showingcompletion of a transition phase into another potentially unmodeledstate of operation by the monitored process or machine;

FIG. 7 illustrates a chart of global similarity values showing atransition from one modeled state to another modeled state of themonitored process or machine;

FIG. 8 illustrates a chart of global similarity values showing atransition from one modeled state to another potentially unmodeled stateof the monitored process or machine;

FIG. 9A symbolically illustrates a similarity operation between acurrent snapshot and the reference library snapshots; and

FIG. 9B illustrates a chart of similarity scores for comparisons in FIG.9A, with a highest similarity indicated.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Turning to FIG. 1, a block diagram is shown of the inventive adaptiveempirical model-based monitoring system. A process or machine 105 isinstrumented with sensors for detecting various physical, statistical,qualitative or logical parameters of the process or machine operation.These are typically provided to the inventive system via an input bus108, exemplified by a FieldBus type bus in a process control system inan industrial plant. The data representing the current state of theprocess or machine 105 is fed from input bus 108 to an estimation engine111, which is coupled to a reference library 114 of past datarepresentative of normal or acceptable states of operation of theprocess or machine. Together, the estimation engine 111 and thereference library 114 comprise the empirical model 117 that models theprocess or machine.

In standard monitoring mode, the sensor data from input bus 108 are alsoprovided to a differencing engine 120 that is disposed to receiveestimates of the current state generated by the empirical model 117 inresponse to input of the actual current state of the process or machine.The differencing engine subtracts for each sensor involved the estimatefrom the actual, and provides these individual outputs to a statisticaltesting module 122 that determines whether the estimate and actualvalues are the same or statistically different, in which case an alertis displayed or otherwise provided to further automated control systems.By way of example, the statistical testing module 122 can be disposed toperform a sequential probability ratio test (SPRT) on each of thedifferenced sensor signals coming from differencing engine 120, toprovide alerts for each signal that is not “normal” or acceptable. Inthis manner, monitoring of the process or machine is carried out basedon the empirical model, providing greater sensitivity and improved leadwarning time for failures.

Current sensor data can also be provided to adaptation decision module125. According to the present invention, this module makes adetermination of whether the current snapshot of sensor data from theprocess or machine 105 represents a process upset or a sensor failure,or in contrast represents the start of a transition to a new operationalstate requiring adaptation of the model, or the stop of a transition toa new operational state. Upon recognizing the start of such atransition, the alert-type output of the empirical model-basedmonitoring can be temporarily suspended to avoid inundating a humanoperator or a downstream control system with unnecessary alertinformation. Upon recognizing the stop of the transition, the adaptationdecision module 125 can enable a retraining module 128 disposed to carryout the actual changes to the reference library 114 necessary to effectadaptation of the model. After the stop of the transition, the processor machine 105 is expected to be in a stabilized new state that may notbe represented in the reference library 114. After the transition iscomplete, the adaptation decision module 125 initiates via theretraining module 128 capture of new snapshots of live data to augmentthe reference library 114. In the event that the reference library growstoo large, or is desirably maintained at a certain size (e.g., forperformance considerations), the vector removal module 131 can weed outold snapshots from the reference library according to certain criteria.Upon completion of the adaptation of the reference library, on-linemonitoring is commenced again.

The invention provides a fully automated model adaptationdecision-making technique for condition based monitoring of a machine orprocess. The adaptation decision module can employ the same similarityoperator techniques that can be employed in the empirical model forgenerating estimates, as discussed below. Accordingly, the currentsensor snapshot from the input bus 108 is compared using the similarityoperator to the estimate generated in response thereto by the estimationengine 111 to generate a similarity score called global similarity forpurposes hereof. This global similarity is itself a signal that can bemonitored and processed snapshot over snapshot. The behavior of theglobal similarity is one means by which the inventive module candistinguish the need for adaptation from a mere process or sensor upset.

Turning now first to the regular monitoring mode of the inventivesurveillance system, an empirical model-based method and system formonitoring a process or machine is described in the aforementioned U.S.Pat. No. 5,764,509 to Gross et al. Implementing such a monitoring systemcomprises two stages, a first stage for building the empirical model(also known as “training”), and a second stage of turning livemonitoring on. Other empirical models that train on known data couldalso be employed, such as neural networks, but for purposes ofillustration, an empirical model along the lines of the Gross patent asa baseline will be described.

A method for training the empirical model is graphically depicted inFIG. 2, wherein collected historic sensor data for the process ormachine is distilled to create a representative training data set, thereference library. Five sensor signals 202, 204, 206, 208 and 210 areshown for a process or machine to be monitored, although it should beunderstood that this is not a limitation on the number of sensors thatcan be monitored using the present invention. The abscissa axis 215 isthe sample number or time stamp of the collected sensor data, where thedata is digitally sampled and the sensor data is temporally correlated.The ordinate axis 220 represents the relative magnitude of each sensorreading over the samples or “snapshots”. Each snapshot represents avector of five elements, one reading for each sensor in that snapshot.Of all the previously collected historic sensor data representing normalor acceptable operation, according to this training method, only thosefive-element snapshots are included in the representative training setthat contain either a minimum or a maximum value for any given sensor.Therefore, for sensor 202, the maximum 225 justifies the inclusion ofthe five sensor values at the intersections of line 230 with each sensorsignal, including maximum 225, in the representative training set, as avector of five elements. Similarly, for sensor 202, the minimum 235justifies the inclusion of the five sensor values at the intersectionsof line 240 with each sensor signal.

Selection of representative data is further depicted in FIG. 3 in step130, data collected representing normal operation has N sensors and Lobservations or snapshots or temporally related sets of sensor data thatcomprise an array X of N rows and L columns. In step 304, a counter ifor element number is initialized to zero, and an observation orsnapshot counter t is initialized to one. Two arrays max and min forcontaining maximum and minimum values respectively across the collecteddata for each sensor are initialized to be vectors each of N elementswhich are set equal to the first column of X. Two additional arrays Tmaxand Tmin for holding the observation number of the maximum and minimumvalue seen in the collected data for each sensor are initialized to bevectors each of N elements, all zero.

In step 307, if the sensor value of sensor i at snapshot t in X isgreater than the maximum yet seen for that sensor in the collected data,max(i) is update to equal the sensor value and Tmax(i) stores the numbert of the observation in step 310. If not, a similar test is done for theminimum for that sensor in steps 314 and 317. The observation counter tis incremented in step 320. In step 322, if all the observations havebeen reviewed for a given sensor (t=L), then t is reset and i isincremented (to find the maximum and minimum for the next sensor) instep 325. If the last sensor has been finished (i=N), step 328, thenredundancies are removed and an array D is created from a subset ofvectors from X.

First, in step 330, counters i and j are initialized to one. In step333, the arrays Tmax and Tmin are concatenated to form a single vectorTtmp having 2N elements. These elements are sorted into ascending (ordescending) order in step 336 to form array T. In step 339, holder tmpis set to the first value in T (an observation number that contains asensor minimum or maximum). The first column of D is set equal to thecolumn of X corresponding to the observation number that is the firstelement of T. In the loop starting with decision step 341, the ithelement of T is compared to the value of tmp that contains the previouselement of T. If they are equal (the corresponding observation vector isa minimum or maximum for more than one sensor), it has already beenincluded in D and need not be included again. Counter i is incrementedin step 350. If they are not equal, D is updated to include the columnfrom X that corresponds to the observation number of T(i) in step 344,and tmp is updated with the value at T(i). The counter j is thenincremented in step 347. In step 352, if all the elements of T have beenchecked, then the distillation into training set D has finished, step355.

A variety of empirical models are considered to be the object of thepresent adaptive decisioning and retraining invention, including neuralnetworks, fuzzy logic models, and the like. All these empirical modelsemploy data from the process or machine under surveillance to model andthereby monitor the process or machine. All are subject to theshortcomings of the historic data provided when the models are built, inview of the graceful aging, settling or previously unencountered statesof the monitored process or machine. By way of an example of applyingthe methods of the current invention, the empirical modeling techniqueof the aforementioned patent to Gross et al., will be described. Thisempirical modeling technique uses a similarity operator, which is alsoan inventively employed in the present invention in the form of globalsimilarity and other adaptation decisioning techniques described herein.Generally, a similarity operation provides a scalar similarity scorescaled between one extreme (typically “1” for “identical”) and anotherextreme (typically “zero” for “completely dissimilar”), upon acomparison of two numbers. More particularly, this can be utilized forcomparing two vectors having equal numbers of elements, where asimilarity score is yielded for comparing each like element of the twovectors, and then averaging or otherwise statistically combining thesimilarity scores into one vector-to-vector similarity score.

The calculations for the similarity operation are now described indetail below. In what follows, the subscript “in” generally correspondsto the actual snapshot obtained from the input bus 108, which maycomprise for example ten real time correlated sensors, and the subscript“out” generally corresponds to estimates generated by the estimationengine 111. The reference library 114 comprises a series of snapshotsselected according to the above-described training method, each snapshotbeing a vector of sensor data, arranged like the input snapshot isarranged. To follow the example then, the reference library wouldcomprise vectors made up of ten elements each. This reference librarywill also be referred to as the matrix D.

The step of providing a representative training set according to thedescription above results in a matrix D of values, having ten rows(corresponding to the ten parameters measured on the process or machine)and a sufficient number n of columns (sets of simultaneous or temporallyrelated sensor readings) to properly represent the full expected dynamicoperating range of the process or machine. While the order of thecolumns does not matter in D, the correspondence of rows to particularsensors must be fixed.

Then, using y_(in) to designate a vector (having ten elements in thisexample) corresponding to the input snapshot from input bus 108, avector y_(out) is generated as the estimate from estimation engine 111having ten elements, according to:{right arrow over (y)} _(out) = D·{right arrow over (W)}where W is a weight vector having as many elements N as there arecolumns in D, generated by:

$\overset{arrow}{W} = \frac{\underset{arrow}{\hat{W}}}{( {\sum\limits_{j = 1}^{N}{\hat{W}(j)}} )}$

$\underset{arrow}{\hat{W}} = {( {{\overset{\_}{D}}^{T} \otimes \overset{\_}{D}} )^{- 1} \cdot ( {{\overset{\_}{D}}^{T} \otimes {\overset{arrow}{y}}_{in}} )}$where the similarity operation is represented by the circle with thecross-hatch inside it. The superscript “T” here represents the transposeof the matrix, and the superscript “−1” represents the inverse of thematrix or resulting array. Importantly, there must be row correspondenceto same sensors for the rows in D, y_(in) and y_(out). That is, if thefirst row of the representative training set matrix D corresponds tovalues for a first sensor on the machine, the first element of y_(in)must also be the current value (if operating in real-time) of that samefirst sensor.

The similarity operation can be selected from a variety of knownoperators that produce a measure of the similarity or numericalcloseness of rows of the first operand to columns of the second operand.The result of the operation is a matrix wherein the element of the ithrow and jth column is determined from the ith row of the first operandand the jth column of the second operand. The resulting element (i,j) isa measure of the sameness of these two vectors. In the presentinvention, the ith row of the first operand generally has elementscorresponding to sensor values for a given temporally related state ofthe process or machine, and the same is true for the jth column of thesecond operand. Effectively, the resulting array of similaritymeasurements represents the similarity of each state vector in oneoperand to each state vector in the other operand.

By way of example, one similarity operator that can be used compares thetwo vectors (the ith row and jth column) on an element-by-element basis.Only corresponding elements are compared, e.g., element (i,m) withelement (m,j) but not element (i,m) with element (n,j). For each suchcomparison, the similarity is equal to the absolute value of the smallerof the two values divided by the larger of the two values. Hence, if thevalues are identical, the similarity is equal to one, and if the valuesare grossly unequal, the similarity approaches zero. When all theelemental similarities are computed, the overall similarity of the twovectors is equal to the average of the elemental similarities. Adifferent statistical combination of the elemental similarities can alsobe used in place of averaging, e.g., median.

Another example of a similarity operator that can be used can beunderstood with reference to FIG. 4. With respect to this similarityoperator, the teachings of U.S. Pat. No. 5,987,399 to Wegerich et al.are relevant, and are incorporated in their entirety by reference. Foreach sensor or physical parameter, a triangle 404 is formed to determinethe similarity between two values for that sensor or parameter. The base407 of the triangle is set to a length equal to the difference betweenthe minimum value 412 observed for that sensor in the entire trainingset, and the maximum value 415 observed for that sensor across theentire training set. An angle Ω is formed above that base 407 to createthe triangle 404. The similarity between any two elements in avector-to-vector operation is then found by plotting the locations ofthe values of the two elements, depicted as X₀ and X₁ in the figure,along the base 407, using at one end the value of the minimum 412 and atthe other end the value of the maximum 415 to scale the base 407. Linesegments 421 and 425 drawn to the locations of X₀ and X₁ on the base 407form an angle θ. The ratio of angle θ to angle Ω gives a measure of thedifference between X₀ and X₁ over the range of values in the trainingset for the sensor in question. Subtracting this ratio, or somealgorithmically modified version of it, from the value of one yields anumber between zero and one that is the measure of the similarity of X₀and X₁.

Any angle size less than 180 degrees and any location for the angleabove the base 407 can be selected for purposes of creating a similaritydomain, but whatever is chosen must be used for all similaritymeasurements corresponding to that particular sensor and physicalparameter of the process or machine. Conversely, differently shapedtriangles 404 can be used for different sensors. One method of selectingthe overall shape of the triangle is to empirically test what shaperesults in consistently most accurate estimated signal results.

For computational efficiency, angle Ω can be made a right angle (notdepicted in the figure). Designating line segment 431 as a height h ofthe angle Ω above the base 407, then angle θ for a givenelement-to-element similarity for element i is given by:

$\theta_{i} = {{\tan^{- 1}( \frac{h}{X_{1}(i)} )} - {\tan^{- 1}( \frac{h}{X_{0}(i)} )}}$Then, the elemental similarity is:

$s_{i} = {1 - \frac{\theta_{i}}{\pi/2}}$As indicated above, the elemental similarities can be statisticallyaveraged or otherwise statistically treated to generate an overallsimilarity of a snapshot to another snapshot, as if called for accordingto the invention.

Yet another class of similarity operator that can be used in the presentinvention involves describing the proximity of one state vector toanother state vector in n-space, where n is the dimensionality of thestate vector of the current snapshot of the monitored process ormachine. If the proximity is comparatively close, the similarity of thetwo state vectors is high, whereas if the proximity is distant or large,the similarity diminishes, ultimately vanishingly. By way of example,Euclidean distance between two state vectors can be used to determinesimilarity. In a process instrumented with 20 sensors for example, theEuclidean distance in 20-dimensional space between the currentlymonitored snapshot, comprising a 20 element state vector, and each statevector in the training set provides a measure of similarity, as shown:

$S = \frac{1}{\lbrack {1 + \frac{ ||{\overset{arrow}{x} - \overset{arrow}{d}} ||^{\lambda}}{c}} \rbrack}$wherein X is the current snapshot, and d is a state vector from thetraining set, and λ and c are user-selectable constants.

Turning now to the adaptive systems and methods of the presentinvention, adaptation decision module 125 generally performs tests onthe current snapshot of sensor data or a sequence of these, to determinewhether or not to adapt to a new operational state of the process ormachine. This determination has inherent in it several more particulardecisions. First, the adaptation decision module must decide whether ornot the entire monitoring apparatus has just initiated monitoring ornot. If monitoring has just started, the adaptation decision module willwait for several snapshots or samples of data for the monitoring tostabilize before making tests to decide to adapt. A second decisionrelated to the overall decision to adapt pertains to whether or not themonitored process or machine has entered a transition or not. Typically,when a process or machine changes states, whether through process upset,machine failure, or merely a change in normal operation, there is aperiod of time during which the monitored sensors provide dynamic data,and the process or machine is neither stably in its old mode nor yetstably in its new target mode. This transition is usually manifested asa transient swing in one or more of the sensor data. The adaptationdecision module waits for the transition to complete before adapting.Therefore, in addition to the second decision of determining when atransition has begun, a third decision that must be made is whether thetransition period is over, and the monitored process or machine is in anew stable operational state. It should be understood that “stable” doesnot mean a state in which all sensor readings are flat, but rather astate that can be reliably recognized by the empirical model, which mayentail dynamic but nonetheless correlated movement of sensor readings. Afourth decision that must be made after a transition is to determine ifthe new stable operational state is one that has not been seen by theempirical model or not. If it has not before been encountered, it is acandidate for adaptation. Finally, a fifth decision that must be made iswhether a new, previously unencountered state is in fact a newacceptable state, or a process or sensor upset.

According to the invention, detection of a transient as evidence of apossible transition out of the current operational state can beperformed using the global similarity operator. The global similarity isthe vector-to-vector similarity score computed from a comparison of thecurrent snapshot from the input bus 108 against the estimate from theestimation engine 111. Typically, the estimate is the estimate generatedin response to the current snapshot, but it is also within the scope ofthe present invention that it can be an estimate generated from a priorsnapshot, such as when the model generates predictive estimates forsensor values. The calculations for generating a vector-to-vectorsimilarity value have already been outlined above. Turning to FIG. 5, achart is shown of a typical global similarity generated for a process ormachine under monitoring. The vertical axis 501 is the global similarityscore, and the horizontal axis 504 is the snapshot or sample number ofthe input snapshots, which are typically sequential in time, but mayalso represent some other ordered presentation of snapshots. An upperlimit 506 and a lower limit 509 are calculated as described below foruse in determining at what point the global similarity line or signal512 indicates a transient. The global similarity score at 516 is showndropping below the limit 509, as are subsequent global similarityscores.

Generally, when a process or machine is operating in a state that isrecognized by the empirical model, the global similarity between theestimate and the current input are high, close to 1, and do not varymuch. The location of the limits 506 and 509 can be user-selected, orcan be automatically selected. One method for automatically selectingthese limits is to collect a series of successive global similaritiesand determine the mean of them and the standard deviation. The limitsare then set at the mean plus or minus a multiple of the standarddeviation. A preferred multiple is 3 times the standard deviation. Thenumber of successive global similarities used can be any statisticallysignificant number over which the process or machine is in a modeledoperational state, and 100–1000 is a reasonable number. A smaller numbermay be reasonable if the sampling rate for monitoring is lower, and insome cases can be in the range of 5–10 where the sampling rate formonitoring is on the order of single digits for the time period in whichthe monitored system or process can substantially deviate from normaloperation.

Yet another way of computing the limits 506 and 509 is as follows. Afteraccumulating a historic data set from which a reference library isselected according to a training method like the Min-Max methoddescribed with reference to FIGS. 2 and 3, the remaining snapshots ofthe historic set that were not selected into the reference library canbe fed in as input to the empirical model, and the global similaritiesfor the estimates generated therefrom can be used. These provide a meanand a standard deviation for setting the limits 506 and 509 as describedabove.

According to yet another way of defining limits 506 and 509, they arenot straight line limits, but instead limits that float a fixed amounton either side of a mean determined over a moving window of globalsimilarities. For example, the standard deviation may be computedaccording to either way described above, and then a multiple of thestandard deviation selected. This is then set around a mean this isdefined as the mean global similarity of the last several snapshots, forexample the last five or the last ten.

When a global similarity at a snapshot 516 travels outside the limits506 or 509, the adaptation decision module recognizes this as atransient. This signifies a likelihood that a transition of themonitored process or machine is starting. The adaptation decision modulecan turn off monitoring, or at least alert generation from statisticaltesting module 122 upon detection of a transient. Further, theadaptation decision module then begins to employ one or more of severaltests that can be used to determine when a transition period ends.

Preferably, upon first detecting the transient, the adaptation decisionmodule places upper limit 506 and lower limit 509 around each subsequentglobal similarity point, using the point as the mean, but still usingthe selected multiple of the prior established standard deviation as thelimits. Each successive point is compared to these limits as set aroundthe “mean” of the last point location. This can be seen in FIG. 6, whichshows a process or machine coming out of transition, as measured byglobal similarity. Point 602 is a transition point. Upper limit 605 andlower limit 608 are shown at some multiple of the prior establishedstandard deviation around the point 602. Point 611 falls outside of thisrange, and therefore is also a transition point. However, point 614falls within the range around point 617, and the adaptation decisionmodule then begins to keep track of these two and subsequent points totest for stability of the global similarity. One way of doing this is tocount a successive number of snapshot global similarities that all fallwithin the range around the prior global similarity, and when aparticular count is reached, determine the process or machine to havestabilized. A typical count that can be used is five snapshots, thoughit will be contingent on the dynamics of the process or machine beingmonitored, and the sample rate at which snapshots are captured. Countingcan start at either point 617 or 614. If a subsequent point fallsoutside of the range, before the chosen count is reached, such as isshown at point 620, the point is determined to be in transition, and thecount is zeroed. Starting at point 620 then, the count would begin againif the point following were within range. Until the count is reached,the process or machine is considered to still be in transition. Anotherway of determining if a the monitored process or machine has stabilizedin a new state is to look for at least a selected number of globalsimilarity scores over a moving window of snapshots that lie within theaforementioned range, where the range is set around the mean of allprevious global similarities in the window for any given globalsimilarity. By way of example, if the window of snapshots is five, andthe selected number in the set of five that must have qualifying globalsimilarities in range is four, then the second global similarity scorequalifies if it lies in the range set around the first globalsimilarity, and the third global similarity qualifies if it lies in therange set around the mean of the first and second global similarities,and so on. If the four global similarities after the first all qualify,then the system has stabilized in a new state. The window can be chosento be much longer, 50 for example, depending on the sampling rate ofmonitoring, and the selected least number of qualifying values can be 40for example.

When a count is reached of in-range global similarity points, as forexample at point 623 after five consecutive in-range points indicated bybox 625, the adaptation decision module indicates the transition is overand a new state has been reached. Note in FIG. 6, the global similarityhas stabilized at a lower overall similarity (around 0.8) than was shownin FIG. 5 (around 0.975), indicating the empirical model is not modelingthis state as well as the previous state, and may in fact not berecognizing the new state at all. Typically, if the new state is alsopart of the empirical model, the global similarity curve will look morelike that shown in FIG. 7. Therein, a first operational state isindicated by global similarities 701. A transition is begun at transient703, and continues until a new stable operational state 705 is reachedbeyond point 706 as indicated by five (or some other preselected number)of consecutive global similarities 710 within range of each other.

According to yet another way for determining adaptation, independent ofthe use of the global similarity, the adaptation decision module canexamine certain of the sensor data that comprise the current snapshotthat are designated by the user usually upon installation as controlvariables in the monitored process or machine. This technique provides amuch simpler way of determining when to adapt, but can only be employedwhen control variables are clearly separable from dependent parametersof the process or machine. A control variable is typically manuallydetermined ahead of model building with domain knowledge of theapplication. Control variables are typically those inputs to a processor machine that drive the operational behavior of the process ormachine. They are often environmental variables over which no controlcan be exerted. For example, in an engine or turbine, ambienttemperature is often a control variable. When training the empiricalmodel, as for example outlined above with the Min-Max method, thesoftware for executing the inventive adaptive monitoring system keepstrack of the overall ranges of the control variables seen in thetraining set from which the reference set is distilled. Then, inmonitoring operation, the adaptive decision module simply compares thecurrent control variable(s) to those ranges, and if one or more controlvariables are now outside the range trained on, the adaptation decisionmodule can initiate retraining. It is also useful to use controlvariables alongside the global similarity operator, so that adetermination can be made when the transition from one state to anotheris over, as described above. Alternatively, standard techniques known inthe art for analyzing the stability of variables can also be employeddirectly against the control variables, if the dynamics of the controlvariables permits, to make the determination of when a transition beginsand ends. In any case, in using control variables to determine when toretrain the empirical model, what is sacrificed is the ability tomonitor those control variables for upset. In other words, if a controlvariable goes outside the range that was trained on, it is assumed bythe inventive apparatus that a new acceptable operational state has beenencountered, rather than assuming that the control variable indicatesabnormal operation.

In the event that this control variable-based decision is not employed,there remains after determining that a transition has stopped, the stepof determining whether the new state is also already sufficientlymodeled, or is a heretofore-unencountered state that must be adapted to.For this the adaptation decision module has a battery of tests that canbe used to make the determination in addition to the control variablerange check.

In a first technique, a threshold may be applied to the mean globalsimilarity of the new state at the end of the transition period. Forexample, with reference to FIG. 8, a mean lower than a 0.90 threshold804 can be used to indicate that the new state is not sufficientlymodeled by the empirical model, and the model must be adapted toaccommodate the new state. According to the invention, the mean can beexamined over one snapshot 808 to make this decision (in which case themean is simply the global similarity), or alternatively can be examinedover a series of snapshots, where the mean is constantly recalculatedusing the most recent 5 results, by way of example. If the mean thenfalls below the selected threshold more than a selected fraction ofsnapshots over a series of snapshots (for example half), then the newstate is considered to be unrecognized and potentially subject toadaptation. For example, as shown in FIG. 8 the five points 812 havefour that fall below the threshold 804, and only one above, andtherefore this state would be considered unrecognized.

According to a second technique, a window of successive globalsimilarity values can be compared to the selected threshold, and if atleast a certain number of these falls below the threshold, then the newstate is considered to be unrecognized and potentially subject toadaptation. For example, if a moving window of five global similaritiesare examined, and at least three are below the threshold, the new statecan be deemed subject to adaptation.

In a third technique depicted in FIGS. 9A and 9B, the actual currentsnapshot can be processed for similarity against the entire referencelibrary (as is done as part of the process of generating the estimate),and a test is performed on the similarities. Given that the adaptationdecision module indicates a new state has been settled into, thesimilarity scores of the current snapshot against the reference librarycan be examined for the highest such similarity, and if this is below achosen threshold, the new state can be deemed an unrecognized state thatis a candidate for adaptation. A typical threshold for this would be inthe range of less than 0.90. As can be seen in FIG. 9A, upon comparingthe current snapshot 903 (symbolized by a vector symbol with dotsstanding in for sensor values) to the snapshots of the reference library114, a series of similarity scores 907 are generated for each comparisonas part of routine monitoring. The scores are shown in a chart in FIG.9B, where a highest similarity score 912 is above the 0.90 threshold,and therefore the current snapshot does not indicate a need to adapt.Again, this decision can be made in one snapshot, or can be made bydetermining whether a selected fraction of more of a series of currentsnapshots have highest reference library similarities that fall belowthe chosen threshold.

Yet a third technique that may be employed to determine if a new statepresents itself and the empirical model must be adapted is to examinethe alarm fraction generated by a SPRT module on the monitoringapparatus.

According to the invention, the global similarity operator has theinherent ability to distinguish between a process or sensor upset and achange of state. When a sensor fails, the empirical model typicallyestimates a reasonable value for what the sensor ought to indicate,based on the other input sensors. The difference between this failedsensor reading and the estimate for it (really an estimate of theunderlying measured parameter) provides a means for statistical testingmodule 122 to alert a human operator that there is a problem at thefailed sensor. However, due to the nature of the similarity operation,the effect on the global similarity is limited. In fact, the mean ofseveral sequential global similarities in the case of a failed sensormay not change much from the mean when the sensor was not failed, thoughthe variance of the global similarity may increase somewhat (yet usuallystill remain within the thresholds 506 and 509 indicated in FIG. 5). Inthis way, the adaptation decision module will generally not attempt toadapt on a failed sensor, and the monitoring system can successfullyalert on the failed sensor.

When a process upset occurs that affects one or only a few of themonitored parameters, the adaptation decision module will similarly notindicate the need to adapt, even though alerts for the upset areoccurring in the monitoring apparatus. This is true even where a processupset eventually leads to significant change in all the variables,because the monitoring apparatus of the present invention is designed tocatch the earliest possible sign of change and alert on it. Long beforethe process upset affects all variables, it is likely the human operatorwould have been notified of the upset.

In addition, a catastrophic process upset usually also fails to exhibita settling into a new state. Global similarities for a severely upsetprocess not only potentially drop to very low levels (less than 0.50)but also suffer from a continuing large variance. Typically, a processupset will fail to settle into a new stable state with the rapidity thata mere operational mode shift will, and this can be used to distinguisha process upset from a new acceptable operational state. This is bestdetermined empirically based on the application, and a user selectablesetting can be provided in the software of the adaptation decisionmodule to designate a period during which a transition must settle intoa stable state or otherwise be considered a process upset and turn onalerting again. According to the invention, the adaptation decisionmodule can also measure the variance of the global similarity, and ifthe variance is still above a certain threshold after a selected timeoutperiod, the transition can be deemed a process upset, and again alertingcan be turned back on in the monitoring coming through statisticaltesting module 122.

After the adaptation decision module has ascertained that:

-   -   1) a control variable is now out of range, and adaptation is        warranted because:        -   a) the global similarity is now below an acceptable            threshold, indicating a new unrecognized state; or        -   b) the highest similarity of the current snapshot or            sequence of snapshots is below an acceptable threshold,            indicating a new unrecognized state; or        -   c) a number of alerts are being generated, starting            coincidentally with the change in the control variable; or    -   2) an adaptation is warranted because:        -   a) a transient was detected on global similarity, signaling            a transition; and        -   b) the transition completed and a new stable state was            realized; and            -   i) the new stable state is not recognized because the                global similarity is now below an acceptable threshold;                or            -   ii) the new stable state is not recognized because the                highest similarity of the current snapshot or sequence                of snapshots is below an acceptable threshold; or            -   iii) the new stable state is not recognized because of                the fraction of alerts that are being generated in                monitoring is above a threshold.                An adaptation step is then carried out by the retrain                module 128. According to the invention, retraining can                be accomplished either by adding snapshots from the                sequence of current snapshots to the reference library,                or by replacing snapshots in the reference library. Both                modes can be used, where the reference library has an                initial size at implementation of the monitoring system,                and a maximum size is selected to which the reference                library can grow, and beyond which newly added snapshots                must replace existing snapshots.

When adding current snapshots to the reference library, the retrainmodule first decides which snapshots to select for addition. Accordingto one embodiment, when the adaptation decision module identifies basedon the global similarity a sequence of several, e.g., 5 snapshots, forwhich the global similarity has stabilized, that is a new state, and thenew state has been determined to be previously unmodeled, the fivesnapshots can be used to augment the reference library. In addition, asof the sixth snapshot, the retrain module begins to go into a longeradaptation cycle, checking the snapshots as they come in and testingusing the global similarity test whether the newly augmented model isadequately modeling the new snapshots. A threshold can again bedetermined, for example 0.90, which the newly augmented referencelibrary must surpass in global similarity, for the adaptation to bedeclared finished. If the threshold is not met, then the retrain modulecontinues to add new snapshots (or at least those snapshots which do notappear to be adequately modeled) as long as the new state is stable andnot a new transient (indicating a new stage of transition or perhapsprocess upset or sensor failure. A set limit to how long a retrainmodule will continue to engage in the longer adaptation cycle beyond theend of a transition can also optionally be set, so that adaptation doesnot continue indefinitely. This may apply to a new state which simplycannot be adequately modeled to the global similarity threshold chosenas the cutoff for adaptation.

According to yet another mode, instead of merely adding the additionalidentified snapshots to the reference library, the entire referencelibrary and the additional snapshots can be combined into a totaltraining set to which a training method such as Min-Max is applied, todistill the new training set into a new reference library.

When the size limit on the reference library is reached, the vectorremoval module can use several methods to replace or remove oldsnapshots (or vectors of sensor data) from the reference library.According to a first way, for each snapshot that will be added beyondthe limit, the vector in the reference library which bears the highestsimilarity to the snapshot desirably added is removed. For thisoperation the similarity operator is used as described herein. Accordingto a second method, upon using a training method such as Min-Max, thetime stamp of when a vector is added to the reference library isexamined through the entire library, and the oldest time stamped vectoris removed. In this case, the replacement snapshot bears the time stampof the moment of replacement, and therefore has the newest time stamp.According to yet another method, during regular monitoring mode of theempirical model-based monitoring apparatus, for each current snapshotfrom the monitored process or machine, a determination is made whichsnapshot in the reference library has the highest similarity to it, andthat snapshot is time-stamped with the moment of comparison. Therefore,each snapshot in the reference library is potentially being updated asbeing the last closest state vector seen in operation of the process ormachine. Then, when adding a new vector as part of adaptation, thevector with the oldest time stamp is replaced. The new replacementsnapshot of course bears a current time stamp. In this way, snapshots inthe reference library representing states the monitoring system has notseen in the longest time, are replaced first with new updated snapshots.This mode is particularly useful when monitoring equipment or processesthat settle gracefully with age, and are not expected to achieve exactlythe operational states they were in when they were brand new.

It will be appreciated by those skilled in the art that modifications tothe foregoing preferred embodiments may be made in various aspects. Thepresent invention is set forth with particularity in any appendedclaims. It is deemed that the spirit and scope of that inventionencompasses such modifications and alterations to the preferredembodiment as would be apparent to one of ordinary skill in the art andfamiliar with the teachings of the present application.

1. A method for adapting an empirical model used in monitoring a system,comprising the steps of: receiving actual values of monitored parameterscharacterizing the system; generating from the empirical model estimatesof the parameter values in response to receiving actual values;calculating a global similarity score for a comparison of a set ofestimated parameter values and a related set of actual parameter values;and adapting the empirical model to account for at least some receivedactual values based on the global similarity score.
 2. A methodaccording to claim 1 where said adapting step comprises adapting theempirical model when a global similarity score falls outside of aselected range.
 3. A method according to claim 1 wherein the adaptingstep comprises: identifying a beginning of a transition phase when aglobal similarity score falls outside of a selected range; identifyingan end of the transition phase; and determining in response toidentification of the end of the transition phase whether the system isin a new state not accounted for in the empirical model; and updatingthe empirical model to account for the new state in response to adetermination that the new state was not accounted for in the empiricalmodel.
 4. A method according to claim 3 wherein the determining stepcomprises comparing at least one global similarity score after the endof the transition phase to a threshold and if it is below the thresholdthen concluding that the new state is not accounted for in the empiricalmodel.
 5. A method according to claim 3 wherein the determining stepcomprises comparing a window of global similarity scores after the endof the transition phase to a threshold and if at least a selected numberof said scores in said window fall below the threshold then concludingthat the new state is not accounted for in the empirical model.
 6. Amethod according to claim 3 wherein the determining step comprisescomparing the mean of a window of global similarity scores after the endof the transition phase to a threshold and if the mean of said scores insaid window falls below the threshold then concluding that the new stateis not accounted for in the empirical model.
 7. A method according toclaim 3 wherein the empirical model has a reference library of snapshotsof parameter values characterizing recognized system states, and thedetermining step comprises comparing a snapshot of received actualvalues after the end of the transition phase to each snapshot in thereference library to compute a similarity for each comparison, and ifthe highest such similarity is less than a selected threshold thenconcluding that the new state is not accounted for in the empiricalmodel.
 8. A method according to claim 3 wherein the empirical model hasa reference library of snapshots of parameter values characterizingrecognized system states, and the determining step comprises comparingeach in a series of snapshots of received actual values after the end ofthe transition phase to each snapshot in the reference library tocompute a similarity for each comparison, and if the mean over theseries of the highest similarity for each actual snapshot is less than aselected threshold then concluding that the new state is not accountedfor in the empirical model.
 9. A method according to claim 3 wherein theempirical model has a reference library of snapshots of parameter valuescharacterizing recognized system states, and the determining stepcomprises comparing each in a series of snapshots of received actualvalues after the end of the transition phase to each snapshot in thereference library to compute a similarity for each comparison, and ifthe highest similarity of at least a selected number of actual snapshotsover the series is less than a selected threshold then concluding thatthe new state is not accounted for in the empirical model.
 10. A methodaccording to claim 3, wherein the step of identifying the end of thetransition phase comprises examining a moving window of successiveglobal similarity scores and when each of at least a selected number ofsuccessive global similarity scores in the window lies within a selectedrange around a previous global similarity score, identifying that theend of transition has been reached.
 11. A method according to claim 3,wherein the step of identifying the end of the transition phasecomprises examining a moving window of successive global similarityscores and when each of at least a selected number of successive globalsimilarity scores in the window lies within a selected range around themean of the previous global similarity scores in the window, identifyingthat the end of transition has been reached.
 12. A method according toclaim 3 wherein the empirical model has a reference library of snapshotsof parameter values characterizing recognized system states, and theupdating step comprises adding at least one snapshot of the receivedactual values to the reference library.
 13. A method according to claim1 wherein a selected range has a lower threshold equal to a multiple ofa standard deviation for a sequence of global similarity scores,subtracted from the mean of the sequence of global similarity scores.14. A method according to claim 1 wherein the calculating step comprisescomparing corresponding elements of a snapshot of received actualparameter values and a related snapshot of estimates to generateelemental similarities, and statistically combining the elementalsimilarities to generate the global similarity score.
 15. A method foradapting a reference data set of an empirical model used in empiricallymodeling a system, comprising the steps of: receiving actual values ofmonitored parameters characterizing the system, including at least onecontrol parameter; determining that the actual value for the at leastone control parameter lies outside a predetermined range; adapting theempirical model in response to said determining step, to account for thereceived actual values.
 16. A method according to claim 15 wherein thereference data set comprises snapshots of time-correlated parametervalues, and said adapting step comprises adding at least one snapshot ofthe received actual values to the reference set.
 17. An apparatus formonitoring an operation of a system characterized by operationalparameters, comprising: an empirical model for generating estimates ofparameter values in response to receiving actual parameter valuescharacterizing the operation of the system; a global similarity enginefor generating a global similarity score for a comparison of a set ofestimated parameter values and a related set of actual parameter valuesfrom said system; and an adaptation module for adapting the empiricalmodel to account for at least some received actual parameter valuesbased on the global similarity score.
 18. An apparatus according toclaim 17 where said adaptation module is disposed to adapt the empiricalmodel when a global similarity score falls outside of a selected range.19. An apparatus according to claim 18 where said empirical modelcomprises a reference library of snapshots of time-correlated parametervalues, and said adaptation module adapts the empirical model by addingat least one snapshot of the received actual values to the referencelibrary.
 20. An apparatus according to claim 17 wherein the adaptationmodule first identifies a transition phase after which it adapts theempirical model to account for at least some received actual parametervalues based on global similarity scores from said global similarityengine.
 21. An apparatus according to claim 20 wherein the adaptationmodule identifies a beginning of the transition phase when a globalsimilarity score falls outside of a selected range.
 22. An apparatusaccording to claim 20, wherein the adaptation module identifies an endof the transition phase by examining a moving window of successiveglobal similarity scores and when each of at least a selected number ofsuccessive global similarity scores in the window lies within a selectedrange around the previous global similarity score, identifying that theend of transition has been reached.
 23. An apparatus according to claim20, wherein the adaptation module identifies the end of the transitionphase by examining a moving window of successive global similarityscores and when each of at least a selected number of successive globalsimilarity scores in the window lies within a selected range around themean of the previous global similarity scores in the window, identifyingthat the end of transition has been reached.
 24. An apparatus accordingto claim 20 wherein the adaptation module determines in response toidentification of the transition phase whether the system is in a newstate not accounted for in the empirical model.
 25. An apparatusaccording to claim 24 wherein the adaptation module determines that thenew state is not accounted for in the empirical model if at least oneglobal similarity score after the end of the transition phase is below aselected threshold.
 26. An apparatus according to claim 24 wherein theadaptation module determines that the new state is not accounted for inthe empirical model if at least a selected number of global similarityscores in a window of global similarity scores after the end of thetransition phase fall below a selected threshold.
 27. An apparatusaccording to claim 24 wherein the adaptation module determines that thenew state is not accounted for in the empirical model if the mean globalsimilarity value over a window of global similarity scores after the endof the transition phase falls below a selected threshold.
 28. Anapparatus according to claim 24 wherein the empirical model comprises areference library of snapshots of parameter values characterizingrecognized system states, and the adaptation module compares a snapshotof received actual values after the end of the transition phase to eachsnapshot in the reference library to compute a similarity for eachcomparison, and if the highest such similarity is less than a selectedthreshold then determines that the new state is not accounted for in theempirical model.
 29. An apparatus according to claim 24 wherein theempirical model comprises a reference library of snapshots of parametervalues characterizing recognized system states, and the adaptationmodule compares each in a series of snapshots of received actual valuesafter the end of the transition phase to each snapshot in the referencelibrary to compute a similarity for each comparison, and if the meanover the series of the highest similarity for each actual snapshot isless than a selected threshold then determines that the new state is notaccounted for in the empirical model.
 30. An apparatus according toclaim 24 wherein the empirical model comprises a reference library ofsnapshots of parameter values characterizing recognized system states,and the adaptation module compares each in a series of snapshots ofreceived actual values after the end of the transition phase to eachsnapshot in the reference library to compute a similarity for eachcomparison, and if the highest similarity of at least a selected numberof actual snapshots over the series is less than a selected thresholdthen determines that the new state is not accounted for in the empiricalmodel.
 31. An apparatus according to claim 21 wherein the selected rangehas a lower threshold equal to a multiple of a standard deviation for asequence of global similarity scores, subtracted from the mean of thesequence of global similarity scores.
 32. A computer program product,said computer program product comprising a computer usable medium havingcomputer readable program code thereon for causing a computer processorto execute a series of instructions, for monitoring an operation of asystem characterized by operational parameters, comprising: computerreadable program code means for receiving actual parameter valuescharacterizing operation of the system, including at least one controlparameter; computer readable program code means for determining that anactual value for the at least one control parameter lies outside apredetermined range; computer readable program code means for storingdata representing an empirical model of the system in a data storagemeans; and computer readable program code means for adapting the storedempirical model data in response to said determining step, to accountfor the received actual values.
 33. A computer program product accordingto claim 32 wherein the empirical model data comprises snapshots oftime-correlated parameter values, and said means for adapting isdisposed to add at least one snapshot of the received actual parametervalues to the empirical model data.
 34. A computer program productaccording to claim 33 further comprising computer readable program meansfor generating estimates of parameter values in response to the meansfor receiving, using the empirical model data in said data storagemeans.
 35. A computer program product according to claim 34 wherein saidmeans for estimating uses a similarity operator.
 36. An apparatus formonitoring a condition of a system selected from the set of a machineand a process, using monitored sensor information from said system,comprising: a memory means for storing an empirical model of sensorinformation characterizing said system; a computer processing unitdisposed to receive actual values of the monitored sensor informationcharacterizing said system, including at least one control parameter,and generate estimates of at least some of the monitored sensorinformation for evaluating the condition of said system; and anadaptation program module executable in said computer processing unitfor adapting the empirical model to account for at least some receivedactual values upon determining that the received actual value for the atleast one control parameter lies outside a predetermined range.
 37. Anapparatus according to claim 36, wherein the empirical model is asimilarity-based model comprising observations of sensor informationcharacterizing said system, and further wherein said adaptation programmodule causes adaptation of the similarity-based model by adding anobservation from the received actual values to the sensor informationcomprising the similarity-based model.